The ACADO toolkit [1]} was used to implement the nonlinear programming-based information-aware local planner running with a replan period of \(12\ \) [s] and the low-level model predictive controller running at \(10\ \) [Hz]. The information-aware mid-level planning scheme of Section REF  was used, with an exponentially decaying weighting on \(\mathbf {\gamma }\)  with time constant \(\tau \)  of \(\frac{1}{10}\)  the global plan horizon. The number of local replans used was 11. A sample run of RATTLE can be seen in Fig. REF , where the global plan is tracked by local plans containing desired levels of information-awareness. As this weighting decreases and estimation accuracy improves, the controller and planner models resemble the system behaviour to a greater degree of accuracy. The parameter estimator used poses and twists from Astrobee's localization algorithm, along with the applied forces and torques as inputs. Estimated parameters were incorporated into the system model of the local planner and controller at a period of \(16\ \) [s]. This avoided updates using transient estimates and controller instabilities due to a rapidly changing system model. The parameter estimation comparison is shown explicitly in Fig. REF , where non-informative plans for three representative runs are shown at left (blue) compared to information-aware plans at right (red). \(\hat{m}\)  and \(\hat{I}_{zz}\)  in particular show improvement in information-aware plans, while poor observability rendered accurate center of mass estimates difficult to obtain for both cases.
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